MétaCan
Menu
Back to cohort
Record W2583158155 · doi:10.1186/s12645-017-0026-0

Biological mechanisms of gold nanoparticle radiosensitization

2017· review· en· W2583158155 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCancer Nanotechnology · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersNational Physical LaboratoryQueen's UniversityQueen's University BelfastEuropean Commission
KeywordsNanotechnologyNanomedicineColloidal goldBiochemical engineeringMaterials scienceComputer scienceNanoparticleEngineering

Abstract

fetched live from OpenAlex

There has been growing interest in the use of nanomaterials for a range of biomedical applications over the last number of years. In particular, gold nanoparticles (GNPs) possess a number of unique properties that make them ideal candidates as radiosensitizers on the basis of their strong photoelectric absorption coefficient and ease of synthesis. However, despite promising preclinical evidence in vitro supported by a limited amount of in vivo experiments, along with advances in mechanistic understanding, GNPs have not yet translated into the clinic. This may be due to disparity between predicted levels of radiosensitization based on physical action, observed biological response and an incomplete mechanistic understanding, alongside current experimental limitations. This paper provides a review of the current state of the field, highlighting the potential underlying biological mechanisms in GNP radiosensitization and examining the barriers to clinical translation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.177
GPT teacher head0.402
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it